How I Shipped 95,000 Proteins in Under 5 Minutes: Building a Scalable Inference Engine for Scientific ML

Scientific machine learning is one of the most important fields of the next decade — but its tooling is still clunky, inconsistent, and painfully slow. Researchers in biology, materials science, and physics often don’t have the infrastructure or time to build robust, scalable inference systems that can generate real results fast.

So I built one.

It’s called Lambda Inference, and it’s a multi-domain inference engine optimized for high-throughput, low-latency prediction. In one session, I used it to generate and infer 95,000 protein sequences in under five minutes. This blog post explains how I did it — from the architecture and tech stack down to the specific function that made it all possible.

Why I Built It

This started from a core frustration: scientific ML tasks — like predicting protein structures or material properties — are powerful in theory but painfully fragmented in practice. There’s no centralized way to plug in domain-specific inputs and receive confidence-ranked predictions from a preloaded, trained model. Tools exist, but they’re scattered across legacy codebases or buried in papers and internal scripts.

I wanted something simple: an engine I could call with scientific input and get a fast, structured, inference-ready output. I wanted it for proteins, for materials, and for astrophysics. So I built it.

How the Inference Works

At the heart of the protein pipeline is a simple function that combines generation and prediction. Here’s a minimal example (stripped down for clarity):

import random

AMINO_ACIDS = "ACDEFGHIKLMNPQRSTVWY"

def generate_protein_sequence(length=12):
    return ''.join(random.choices(AMINO_ACIDS, k=length))

def predict_structure(model, sequence, threshold=0.8):
    pred = model.predict({"sequence": sequence})
    confidence = pred.get("confidence", 0)
    if confidence >= threshold:
        return {"sequence": sequence, "structure": pred["structure"], "confidence": confidence}
    return None

def generate_and_infer(model, num_sequences=100000):
    outputs = []
    for _ in range(num_sequences):
        seq = generate_protein_sequence()
        result = predict_structure(model, seq)
        if result:
            outputs.append(result)
    return outputs

This basic loop, with some threading and GPU optimization, was enough to produce and filter 95,000 sequences in under five minutes. Results were written in Arrow format, compressed, and uploaded to Hugging Face under the Nexa ecosystem.

What Components I Used

Here’s a breakdown of the actual tech stack I used in production:

FastAPI for REST endpoints across /bio, /astro, and /materials

PyTorch for running all model inference (models loaded into memory once)

Docker for containerization and portability

Arrow + Pandas for fast serialization of large outputs

Redis + Postgres for caching and request logging

Plotly + Streamlit (via LambdaViz) for rendering 3D structures

Hugging Face Spaces to make everything accessible from a browser

Everything was orchestrated locally on a T4 GPU instance, with CPU threading for sequence generation and filtering.

What's the Minimal Tech You Actually Need?

If you want to build a barebones scientific inference engine like this, here’s the absolute minimum:

A trained model checkpoint (PyTorch or ONNX)

A Python prediction function (like above) that can handle inputs and return outputs + confidence

A simple script to loop through inputs, run inference, and filter by confidence

FastAPI (or Flask) to expose a REST API if needed

Arrow (or CSV/JSON) for storing the results

You can run this entire system on:

1 GPU-enabled machine (T4, A10, or even CPU if small)

A single Docker container

Less than 2GB RAM usage during inference

No frontend — just curl or Python scripts calling the API

And you can build and deploy that in a weekend.

What the Results Show

This was more than a benchmark — it was a signal. When you combine model inference with fast data generation and thoughtful engineering, you don’t need a 10-person team to ship valuable scientific assets.

I shipped:

95,000 protein structures

In under 300 seconds

With confidence filtering

Structured in training-ready format

And I did it with a single model, a single machine, and ~150 lines of core logic.

Why This Matters

Inference isn’t just a backend process — it’s the beginning of what enables researchers to test ideas, run simulations, and fine-tune models on real-world scientific problems. Without fast inference infra, everything breaks: training becomes slower, data pipelines get blocked, and your modeling loop stalls out.

What I’ve built with Lambda Inference is one layer of a much larger mission: to build the infrastructure for high-quality, domain-specific scientific ML at scale.

This engine now supports biological predictions, materials property estimation, and stellar astrophysics regressors. More models are being added. And with each domain, the same philosophy applies: serve structured, validated predictions fast and let researchers focus on science — not sysadmin work.

Try It Yourself

You can try the engine or use the protein dataset:

Lambda Inference (HF Demo)

95K Protein Dataset on Hugging Face

Final Note

If you're a researcher, startup, or lab working in a domain that could benefit from plug-and-play ML inference — reach out. I build custom datasets, fine-tuned models, and deployable inference pipelines.

This was just one experiment. But the goal is bigger: to make scientific machine learning feel like productized software — fast, elegant, useful.

Let’s build it.
link to the repo for more deatils:
https://github.com/DarkStarStrix/Lambda_Inference